UNLABELLED: We introduce a novel, sound, sample-efficient, and highly-scalable algorithm for variable selection for classification, regression and prediction called HITON. The algorithm works by inducing the Markov Blanket of the variable to be classified or predicted. A wide variety of biomedical tasks with different characteristics were used for an empirical evaluation. Namely, (i) bioactivity prediction for drug discovery, (ii) clinical diagnosis of arrhythmias, (iii) bibliographic text categorization, (iv) lung cancer diagnosis from gene expression array data, and (v) proteomics-based prostate cancer detection. State-of-the-art algorithms for each domain were selected for baseline comparison. RESULTS: (1) HITON reduces the number of variables in the prediction models by three orders of magnitude relative to the original variable set while improving or maintaining accuracy. (2) HITON outperforms the baseline algorithms by selecting more than two orders-of-magnitude smaller variable sets than the baselines, in the selected tasks and datasets.
UNLABELLED: We introduce a novel, sound, sample-efficient, and highly-scalable algorithm for variable selection for classification, regression and prediction called HITON. The algorithm works by inducing the Markov Blanket of the variable to be classified or predicted. A wide variety of biomedical tasks with different characteristics were used for an empirical evaluation. Namely, (i) bioactivity prediction for drug discovery, (ii) clinical diagnosis of arrhythmias, (iii) bibliographic text categorization, (iv) lung cancer diagnosis from gene expression array data, and (v) proteomics-based prostate cancer detection. State-of-the-art algorithms for each domain were selected for baseline comparison. RESULTS: (1) HITON reduces the number of variables in the prediction models by three orders of magnitude relative to the original variable set while improving or maintaining accuracy. (2) HITON outperforms the baseline algorithms by selecting more than two orders-of-magnitude smaller variable sets than the baselines, in the selected tasks and datasets.
Authors: G F Cooper; C F Aliferis; R Ambrosino; J Aronis; B G Buchanan; R Caruana; M J Fine; C Glymour; G Gordon; B H Hanusa; J E Janosky; C Meek; T Mitchell; T Richardson; P Spirtes Journal: Artif Intell Med Date: 1997-02 Impact factor: 5.326
Authors: A Bhattacharjee; W G Richards; J Staunton; C Li; S Monti; P Vasa; C Ladd; J Beheshti; R Bueno; M Gillette; M Loda; G Weber; E J Mark; E S Lander; W Wong; B E Johnson; T R Golub; D J Sugarbaker; M Meyerson Journal: Proc Natl Acad Sci U S A Date: 2001-11-13 Impact factor: 11.205
Authors: Bao-Ling Adam; Yinsheng Qu; John W Davis; Michael D Ward; Mary Ann Clements; Lisa H Cazares; O John Semmes; Paul F Schellhammer; Yutaka Yasui; Ziding Feng; George L Wright Journal: Cancer Res Date: 2002-07-01 Impact factor: 12.701
Authors: Yindalon Aphinyanaphongs; Ioannis Tsamardinos; Alexander Statnikov; Douglas Hardin; Constantin F Aliferis Journal: J Am Med Inform Assoc Date: 2004-11-23 Impact factor: 4.497